Cross Hyperspectral and LiDAR Attention Transformer: An Extended Self-Attention for Land Use and Land Cover Classification
Bibliographic record
Abstract
The successes of attention-driven deep models like the Vision Transformer (ViT) have sparked interest in cross-domain exploration. However, current transformer-based techniques in remote sensing primarily focus on single-modal data, limiting their potential to exploit the growing array of multimodal Earth observation data fully. Enhancing these models for multimodal integration is crucial for comprehensive remote sensing applications. To achieve this, we extend the traditional self-attention mechanism by introducing Cross Hyperspectral and LiDAR (Cross-HL) attention. We present a novel multimodal deep learning framework that effectively fuses remote sensing (RS) data, intending to improve land use and land cover (LULC) recognition. To enhance the accurate exchange of information across different modalities, we fuse their patch projections using the Cross-HL self-attention module. In this process, LiDAR patch tokens serve as queries (Q), while keys (K) and values (V) are derived from HS patch tokens. To demonstrate the superiority of Cross-HL in the proposed multimodal deep learning framework, we conducted extensive experiments on three multimodal RS benchmark datasets: Houston, Trento, and MUUFL. These datasets contain hyperspectral and light detection and ranging (LiDAR) data. The source code for Cross-HL will be made available publicly at https://github.com/AtriSukul1508/Cross-HL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".